Pandemic Darlings The pandemic economy, in original documents
Home Court filings Bofa Ca Unemployment In re: Bank of America California Unemployment Benefits Litigation — S.D. Cal., No. 21-md-02992 Exhibit 161 — In re Bank of America California Unemployment Benefits Litigation (Dkt. 378-6, S.D. Cal. No. 3:21-md-02992)

Court filing

Exhibit 161 — In re Bank of America California Unemployment Benefits Litigation (Dkt. 378-6, S.D. Cal. No. 3:21-md-02992)

Filed November 21, 2024 in In re Bank of America California Unemployment Benefits Litigation; one of 1415 filings from this case.

Record facts

CourtU.S. District Court for the Southern District of California
Filed2024-11-21

U.S. District Court for the Southern District of California · No. 3:21-md-02992-GPC-MSB · Doc. 378-6 · 2024-11-21 · Docket on CourtListener

Full text

Exhibit 161 
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                                                                                                                                        Confidential 
 
UNITED STATES DISTRICT COURTY 
SOUTHERN DISTRICT OF CALIFORNIA 
IN RE BANK OF AMERICA CALIFORNIA 
UNEMPLOYMENT BENEFITS 
LITIGATION 
 
 
Case No. 3:21-md-02992-GPC-MSB 
 
 
 
 
 
 
 
EXPERT REBUTTAL REPORT OF JAY MINNUCCI 
November 21, 2024 
 
 
 
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i 
TABLE OF CONTENTS 
I. 
INTRODUCTION  ...........................................................................................................1 
II. 
SUMMARY OF OPINIONS ............................................................................................2 
III. 
MR. HINDLE IS NOT QUALIFIED TO OPINE ON CALL CENTER STAFFING, 
FORECASTING, OR PERFORMANCE MANAGEMENT ...........................................3 
IV. 
SUBSTANTIALLY ALL CUSTOMER SERVICE CLASS MEMBERS 
EXPERIENCED LENGTHY WAIT TIMES FAR EXCEEDING ANY INDUSTRY 
STANDARDS ..................................................................................................................4 
V. 
THE BANK’S SUBSTANDARD CLAIMS CALL CENTER PERFORMANCE IN 
FALL 2020 CANNOT BE BLAMED ON UNANTICIPATED PANDEMIC-
RELATED EVENTS ........................................................................................................5 
VI. 
WAIT TIMES FOR EDD CARDHOLDERS WERE THE SAME AS—OR WORSE 
THAN—WAIT TIMES FOR OTHER PREPAID CARDHOLDERS .............................9 
VII. 
IT IS STANDARD PRACTICE TO USE MULTI-INDUSTRY BENCHMARKS 
AND STRATEGIES TO EVALUATE CALL CENTER PERFORMANCE ................11 
VIII. THE SYSTEMS TO MAINTAIN AND ACCESS INDIVIDUAL CALL 
WAIT TIME ARE IN NEAR-UNIVERSAL USE AMONG LARGE 
CALL CENTERS ...........................................................................................................16 
 
Appendix A:  Supplemental Materials List ................................................................................21 
 
 
 
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                                                                                                                                        Confidential 
 
1 
I. 
INTRODUCTION 
1. 
I have been retained as an expert witness in this matter by Cotchett, Pitre & 
McCarthy LLP and Altshuler Berzon LLP, co-lead counsel for the Class Plaintiffs. On August 
29, 2024, I provided my initial expert report in this matter (“Minnucci Report”).  
2. 
Based on more than forty years of experience, I described in my Minnucci Report 
the call forecasting, staffing, data management, and security practices that are standard across the 
call center industry. I also offered the opinions that from September 13, 2020 through November 
21, 2020, 
 
 
 
 
 
 
 
 
 
 
. 
3. 
I have reviewed the October 24, 2024 Declaration of Kelley Lorenzen (“Lorenzen 
Decl.”) and the October 24, 2024 Expert Declaration of Steven Hindle (“Hindle Report”), and 
documents cited therein. I stand by all of the opinions expressed in my August 29, 2024 
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2 
Minnucci Report, none of which have been altered by my review of the Hindle Report or 
Lorenzen Declaration.1 
4. 
In this report, I evaluate the portions of the Hindle Report that refer or relate to 
my previous report, noting my agreement with some parts of some of his opinions, while noting 
my disagreement with many others. 
II. 
SUMMARY OF OPINIONS 
5. 
Evaluating the reasonableness of the Bank’s call center staffing and procedures 
and assessing the degree of the Bank’s deviation from industry norms requires substantial 
experience with and knowledge of call center performance metrics, forecasting processes, and 
staffing practices. To the extent that Mr. Hindle’s CV is an accurate description of his 
background, he lacks such experience and is not qualified to opine on whether the Bank’s call 
center staffing, forecasting, and performance management decisions were reasonable or in 
conformity with industry standards.2 
6. 
 Although 
 
for EDD debit cardholders who called the Bank’s Claims call center between September 13, 
2020 and November 21, 2020, 
 
 
.3  
7. 
 
 
1 I have no changes to my CV or compensation, nor additional publications or testimony, to 
disclose. 
2 See infra ¶¶11-12. 
3 See infra ¶¶13-14. 
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3 
.4  
8. 
 
 
.5  
9. 
Because callers compare their experiences—and form their reasonable 
expectations—based on interactions with call centers in different industries, it is standard 
practice for call center leaders to compare across industries when establishing performance 
targets. It is also true that callers will contact whichever of a company’s call centers they 
anticipate will have the shortest wait; as such, it is standard practice—
 
—to establish similar, if not identical, wait time targets across generalist and specialty 
call centers. Accordingly, multi-industry call center performance data across call center types, 
like the data contained in the 2021 U.S. Contact Center Decision-Maker’s Guide, is the best 
source for identifying industry-standard performance benchmarks that apply to even specialty 
call centers like the Bank’s Claims call center.6 
10. 
As long as the Bank did not destroy the relevant data, 
 
.7 
III. 
MR. HINDLE IS NOT QUALIFIED TO OPINE ON CALL CENTER STAFFING, 
FORECASTING, OR PERFORMANCE MANAGEMENT. 
11. 
Mr. Hindle’s CV mentions a position he held in which he was “supporting” 
160,000 contact center employees, which is an appropriate and commonly used term for 
someone who oversees the Information Technology used by contact center staff. In the body of 
 
4 See infra ¶¶ 15-22. 
5 See infra ¶¶ 23-27. 
6 See infra ¶¶ 28-35. 
7 See infra ¶¶ 36-43. 
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4 
his report, he also states that he “oversaw” 160,000 employees,8 which suggests that he was in 
the direct line of supervision of call center managers, supervisors, and agents. However, nothing 
in his CV or Report suggests that he supervised call center managers, supervisors, or agents. 
During the time he worked at the various outsourcing organizations he identifies, the 
accomplishments he lists are almost exclusively in the realm of cybersecurity, business 
continuity, and compliance assurance. 
12. 
Providing an opinion on call center speed of answer performance requires a 
thorough understanding of the data maintained by and metrics used in call centers, typical 
industry results, the most reliable forecasting processes used to predict future call volume, and 
the staffing practices and timing required to prepare and maintain staff levels appropriate for a 
predicted workload. Mr. Hindle’s CV does not demonstrate the requisite level of expertise 
regarding any of these topics.  
IV. 
SUBSTANTIALLY ALL CUSTOMER SERVICE CLASS MEMBERS 
EXPERIENCED LENGTHY WAIT TIMES FAR EXCEEDING ANY INDUSTRY 
STANDARDS. 
13. 
The Hindle Report stated: “[A]verage ASAs cannot be used to represent the wait 
time experienced by individual class members who called during that week either. In my 
experience, each individual caller’s experience varies. There will be individual data points both 
above and below the average, and those individual wait times can be very different than the 
‘average.’”9 That Report also states: “Neither Mr. Regan nor Mr. Minnucci consider how many 
proposed class members experienced wait times either above or below the ASA.”10 Although it 
is mathematically true that an average, by definition, does not reflect each individual’s personal 
 
8 Hindle Rep. ¶ 4. 
9 Hindle Rep. ¶ 16. 
10 Hindle Rep. ¶ 17. 
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6 
 
the Bank then 
 
.14 My report focused on the September to November 2020 
time frame, and concluded that the Bank’s decision to 
 
 
 
.  
17. 
As early as April 2020, the Bank 
 
 
.15 
 
 
 
. Although the Bank 
because it was 
 
 
. Indeed, during a brief five-week period in July and early August,
 
 
 
  
18. 
Nonetheless, from the week of July 5, 2020 to the week of September 27, 2020 
 
 
14 Minnucci Rep. ¶¶ 59-65. 
15 Minnucci Rep. ¶¶ 56-57; see also Ex. 133 at -60339. 
16 See Ex. 18 (Rule 30(b)(6) Depo. of William Golden (“Golden Tr.”)) 67:8-68:6. 
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10 
. The Bank’s 
 
 
, it logically follows that any substantially large sub-group of these 
callers will experience the same average wait times. As I stated in my Report—and as the Hindle 
Report does not 
 
.31 As a result, during any given period, EDD cardholders 
 
. 
25. 
In light of the data provided by the Bank, 
 
 
. The Bank’s records show that, between September 13 and November 21, 2020, 
.32  Because calls 
from EDD debit cardholders 
 
 
 
. 
26. 
If there were any variations in service, in my opinion it is likely that EDD 
cardholders would have received worse service than other prepaid cardholders received. In my 
experience, the only callers who experience meaningfully shorter wait times when contacting a 
severely understaffed center are those who place their calls as soon as the call center begins 
operations each day. This opinion is consistent with the source cited in the Hindle Report, which 
states that “7 AM is the best time to call customer service” and that call center wait times 
 
30 See Hindle Rep. ¶ 15. 
31 Minnucci Rep. ¶¶28-29, tbl. 1. 
32 Ex. 124 at -719115. 
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11 
significantly increase after noon.33 Because the Bank’s Claims call center was open from 8:00 
am to 10:00 pm Eastern Time Monday through Friday and 9:30 am to 8:00 pm Eastern Time on 
Saturday,34 callers living in the Pacific Time Zone, which would include most California EDD 
cardholders, would have had to call at 5:00 am Pacific Time to receive the faster early morning 
service.35 And while EDD callers were likely under-represented in these early morning hours, 
they were likely over-represented at the 7:00 pm Pacific close of the business day. Unfortunately, 
any callers in queue at the end of the day would have been dropped from the queue when the last 
CSR signed out, regardless of how long they had waited, adding to those EDD callers’ 
frustration.36 
27. 
Further, to the extent that the Bank’s call center leaders could influence whether 
EDD cardholders received better or worse service than other prepaid cardholders, 
 
 
37  
VII. 
IT IS STANDARD PRACTICE TO USE MULTI-INDUSTRY BENCHMARKS 
AND STRATEGIES TO EVALUATE CALL CENTER PERFORMANCE. 
28. 
The Hindle Report stated: “[T]he expectations of target ASAs and realized ASAs 
vary depending on the industry, contracts, and the nature and complexity of the requests handled. 
For example, in my experience, wait times can be very different for call centers that handle 
general inquiries about account balance and certain transactions and those that handle more 
complex requests such as unauthorized transactions and account security. In my view, there is no 
 
33 See Hindle Rep. ¶ 16 n.16 (citing TalkDesk, 7 Tips for Getting Better Customer Service, 
https://www.talkdesk.com/resources/infographics/tips-for-getting-better-customer-service). 
34 Ex. 170 at -1362 ¶ 3. 
35 See, e.g., Ex. 12 (Aug. 19, 2024 Willrich Decl.) ¶ 7. 
36 See, e.g., id. ¶ 6; Ex. 10 (July 27, 2024 Oosthuizen Decl.) ¶ 5. 
37 Ex. 77 at -118438. 
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13 
industries. In my consulting practice, I have found the most aggressive ASA targets set at 10 
seconds and the least aggressive at 300 seconds, while the most aggressive abandoned targets are 
2% with the least aggressive at 10%. These targets represent the extremes for individual call 
centers; industry averages have much less variation between the most and least aggressive. 
31. 
Callers develop their expectations regarding how long it should take for a call to 
be answered based on their collective experience with all call centers over time, not just call 
centers in the same industry or providing the same service. In other words, when an individual 
evaluates how quickly Comcast picked up their call, they would not compare Comcast only to 
Verizon and AT&T. They would also compare it to AllState, Wells Fargo, Aetna, Wayfair, and 
any other company that they may have called recently. Because all call center experiences are 
used by customers in evaluating performance, it is standard practice for call centers to consider 
cross-industry benchmarks when setting performance targets. Therefore, the other vertical 
markets mentioned in Table 1 of the Hindle Report are relevant in the trier of fact’s 
consideration of what constitutes an appropriate benchmark for adequate call center performance 
in this case. Accordingly, although there may be some differences in performance across 
industries, it is appropriate to use a multi-industry composite to identify adequate performance 
targets.  
32. 
The Hindle Report significantly overstates the importance of the travel industry in 
the 2021 U.S. Contact Decision-Makers Guide. First, Transport & Travel respondents made up 
only 5% of the 2020 survey, while the Finance industry had the greatest representation among 
respondents.41 Indeed, 29 (14%) of the respondent call centers were in the Finance field—the 
largest group of respondents—while another 28 (13%) were call center outsourcers, similar to 
 
41 Ex. 172 (2021 Decision Makers Guide) at 15. 
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14 
TTEC, Sykes, and ACT, which operated the Bank’s prepaid call centers.42  Even if one assumes 
that the other industries are not relevant, there remain 57 respondents—27%—that are directly 
similar to the Bank. The 2021 Decision-Makers Guide is therefore far more heavily weighted 
toward call centers similar to the Bank than toward Transport & Travel, or any other industry. 
The Hindle Report also overstates the extent to which Transport & Travel respondents were 
likely to have meaningfully different speed of answer performance than the Bank. Even if it were 
true that the Transport & Travel respondents saw unusually low call volume during some parts of 
202043—and it is not clear from the Hindle Report that any supporting evidence exists for such 
an assumption—any decrease in call volume was likely matched with a decrease in staffing, as it 
was widely reported that airlines and other travel business inflicted prolonged wait times on their 
customers during the early months of the pandemic.44 That should not be surprising, as it is 
standard practice for businesses to reduce call center staffing commensurate with substantial 
reductions in call volumes because by doing so, the businesses avoid the expense of over-
staffing. Accordingly, there is no basis for the Hindle Report’s suggestion that the Travel & 
 
42 Id. 
43 Hindle Rep. ¶ 23. 
44 See, e.g., Hannah Klein, Slate, How Long Does it Take to Ask Delta a Question (June 16, 
2020), https://slate.com/business/2020/06/delta-airlines-customer-service.html (describing 
Delta’s graceful disconnect practice and wait times exceeding four hours in June 2020); Matt 
Hochberg, Royal Caribbean Blog, Royal Caribbean Hires Back over 100 Laid off Workers to 
Help with Long phone Hold Times (May 27, 2020), 
https://www.royalcaribbeanblog.com/2020/05/27/royal-caribbean-hires-back-over-100-laid-
workers-help-long-phone-hold-times (describing Royal Caribbean cruises efforts to address 
“longer than normal wait times” driven by cruise cancellation announcements and customer 
service lay offs); Zach Honig, Points Guy, Having Trouble Getting through to Delta? You’re Not 
Alone (June 4, 2020), https://thepointsguy.com/news/how-to-reach-delta (describing the 
difficulties of Delta customers and noting that “current call center issues are related to a staffing 
shortage, after [Delta] asked agents to take a leave of absence in an effort to control costs”). 
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15 
Transport respondents to the U.S. Decision-Makers report had meaningfully different 
performance on the metrics at issue here: average speed to answer and abandonment rate.  
33. 
Although it is my opinion, for the reasons stated above, that multi-industry call 
center performance data is the best source for identifying an ASA benchmark applicable to the 
Bank’s Claims call center, performance data specific to the Finance and Outsourcing industries is 
available.45  The average ASA reported in 2020 across the 29 Finance call centers surveyed by 
ContactBabel was 145 seconds (2.42 minutes).46  The average ASA reported in 2020 across the 
28 Outsourcers surveyed by ContactBabel was 39 seconds.47  
34. 
From September 13, 2020 to November 21, 2020, the average ASA at the Claims 
call center was 
. Even if one were to limit the comparison group to the 29 
Finance call centers in the survey, the Bank 
 
 the 2 minute 25 second average of 
the Finance call center peer group. Regardless of whether one compares the Bank’s Claims call 
center to the Finance group, the Outsourcing group, or all call centers, 
 
.  
35. 
The 1.25 minute benchmark identified in my Report is an appropriate, industry 
standard ASA.48 Although some businesses may set a faster or slower ASA target, those 
variations are minimal. I have never encountered a call center that set an ASA target of more 
 
45 See Ex. 169 (2024 U.S. Contact Center Verticals: Finance, ContactBabel); Ex. 173 (2024 U.S. 
Contact Center Verticals:  Outsourcing, ContactBabel). 
46 See Ex. 169 (Finance Vertical) at 31. 
47 See Ex. 173 (Outsourcing Vertical) at 32. 
48 Indeed, Mr. Hindle’s own source suggests a much lower benchmark. See Hindle ¶ 16 n.16; 
TalkDesk, 7 Tips for Getting Better Customer Service, 
https://www.talkdesk.com/resources/infographics/tips-for-getting-better-customer-service/ 
(indicating that the “average speed to answer is 8.2 seconds”).  
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16 
than five minutes, and no reputable business would 
 
.  
VIII. THE SYSTEMS TO MAINTAIN AND ACCESS INDIVIDUAL CALL WAIT 
TIME ARE IN NEAR-UNIVERSAL USE AMONG LARGE CALL CENTERS. 
36. 
The Hindle Report stated: “[T]he Claim call center systems of record between 
September 13, 2020 and November 21, 2020 did not contain or retain data or information 
showing the time a particular, individual EDD prepaid cardholder spent on hold when calling the 
Claims call center during the Proposed Class Period. This is consistent with my experience that 
while call centers may use [individual caller Automatic Number Identifications] ANI, they 
typically do not retain . . . wait time[] associated with particular ANIs or account holders.”49 The 
Hindle Report bases this opinion on a statement in the Lorenzen Declaration that stated: “None 
of the systems of record during the relevant time period that store data related to these customer 
service calls recorded data regarding individual call wait times, the speed to answer, or call 
handle times. This information is not available on a per caller basis identifiable through Caller 
ID, Automatic Number Identification, Alias ID, or any other individual identifier.”50  
37. 
Neither the Hindle Report nor the Lorenzen Declaration actually responds to the 
point I was making in my Report, which was that the “share of cardholders calling the Claims 
call center who are members of Plaintiffs’ Customer Service class and assert an injury from the 
Bank’s conduct 
” because the Bank’s 
phone systems 
 
51  
 
49 Hindle Rep. ¶ 18 (citing Lorenzen Decl. ¶ 13).  
50 Lorenzen Decl. ¶ 13. 
51 Minnucci Report ¶ 94; see, e.g. Ex. 174 at -1044.  
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18 
person who made the call (known in the industry as the ANI), which is used to the link a call 
record with a specific cardholder.57 The database also contains an item called QUEUETIME, 
which provides the time the call segment spent in queue before being answered.58  To determine 
the amount of time a specific caller waited in the Claims call center queue, one could query the 
database with the caller’s ANI, and once the record is located the time the caller spent in queue 
would be provided. Unless the Bank failed to preserve this data, Avaya’s Call Record Database 
can be used to identify the precise wait time of each member of the Customer Service class. 
41. 
There is evidence that the Bank used 
 
 
. For instance, 
 
 
59 This 
 could only have been 
, as the Bank 
does not 
.60  
 
 the Agent Trace database and the same Call Record Database that has the 
amount of time the caller spent queueing. 
42. 
Although the Call Record Database from the phone system is the most accurate 
way to determine individual caller wait times, individual caller wait times can also be 
mechanically calculated by subtracting handle time, available in the Bank’s call recording 
system, from total call time, derived from the date and time stamps in the Visa Prepaid 
Administration system (Visa PAS). I’ve used records that the Bank produced for Class 
 
57 Id. at 49, 181. 
58 Id. at 50, 280. 
59 Ex. 177 at -190443. 
60 The specific database item accessed to make this determination would have been the 
AGT_RELEASED database item, available in the Call Record Database. See Ex. 176 at 48, 161. 
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21 
Appendix A: Supplemental Materials List 
In addition to sources cited in the Report and Appendix D, I considered the following in 
developing my opinions: 
Declarations and Deposition Transcripts and Accompanying Exhibits 
Declaration of Stephen Hindle, October 24, 2024 
Declaration of Kelley Lorenzen, October 24, 2024 
Call Center Industry Documents and Other External Sources 
Avaya Call Management System Database Items and Calculations, July 2016 release 
U.S. Contact Center Verticals: Finance, ContactBabel, 2024 
U.S. Contact Center Verticals: Outsourcing, ContactBabel, 2024 
https://slate.com/business/2020/06/delta-airlines-customer-service.html 
https://www.royalcaribbeanblog.com/2020/05/27/royal-caribbean-hires-back-over-100-laid-
workers-help-long-phone-hold-times 
https://www.talkdesk.com/resources/infographics/tips-for-getting-better-customer-service/   
https://thepointsguy.com/news/how-to-reach-delta  
Pleadings and Other Case Documents  
Defendant’s Memorandum of Points and Authorities in Opposition to Plaintiffs’ Motion for 
Class Certification, In re: Bank of America California Unemployment Benefits Litig., No. 3:21-
md-02992-GPC-MSB, United States District Court for the Southern District of California, 
October 24, 2024 
February 9, 2024 Letter from Bank Counsel to Plaintiffs Counsel re: Call Center Data 
Documents Produced by Defendant 
BANA_EDD_MDL-00001044 
BANA_EDD_MDL-00001361 
BANA_EDD_MDL-00056916 
BANA_EDD_MDL-00190443 
BANA_EDD_MDL-00291083 
BANA_EDD_MDL-00809945 
BANA_EDD_MDL-00812294 
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